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bayesian population decoding of spiking neurons

FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2009年第OCT期3卷 21页

作者： Gerwinn, Sebastian Macke, Jakob Bethge, Matthias Max Planck Inst Biol Cybernet Computat Vis & Neurosci Grp D-72076 Tubingen Germany

The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a 'spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

关键词： bayesian decoding population coding spiking neurons approximate inference

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Impact of conflicts between long- and short-term priors on the weighted prior integration in visual perception

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COGNITION 2025年 254卷 106006页

作者： Sun, Qi Gong, Xiu-Mei Sun, Qian Zhejiang Normal Univ Sch Psychol Jinhua Peoples R China Intelligent Lab Zhejiang Prov Mental Hlth & Crisis Jinhua Peoples R China

The prior distribution of values for a specific feature can be categorized as long- or short-term priors based on their respective learning durations. Studies have demonstrated that the visual system can integrate both priors through weighted averaging and then utilize the integrated prior to efficiently encode stimuli. It is unclear what determines the two priors' relative weights. To address this question, we arranged the orientations according to three distributions: natural, anti-natural, and natural with increased-amplitude distributions. The natural distribution mirrors the distribution of orientations in the natural world, so it does not conflict with the long-term prior;according to the Kullback-Leibler divergence analysis, the natural distribution had a higher conflict with the anti-natural distribution than with the natural distribution with increased amplitude. It was found that the cardinal bias - the orientation estimates are biased away from the cardinal orientations - was strongest in the natural distribution with increased amplitude but weakest in the anti-natural distribution. These results were accurately predicted by an efficient bayesian observer model in which the prior is the weighted integration of the long- and short-term priors. Importantly, the weight of the short-term prior in the new prior decreased as the level of the conflict between the long- and short-term priors increased. Therefore, this study reveals that the visual system integrates the long- and short-term priors through weighted averaging, with the conflicting level between the two priors determining their relative weights in the integration prior. The integrated prior was used by visual systems to efficiently encode stimuli.

关键词： Efficient coding bayesian decoding Short-term prior Long-term prior Weighted integration Visual perception

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Sleep stages antagonistically modulate reactivation drift

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Neuron 2025年第9期113卷 1446-1459.e6页

作者： Bollmann, Lars Baracskay, Peter Stella, Federico Csicsvari, Jozsef Institute of Science and Technology (ISTA) Austria Klosterneuburg 3400 Austria Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen 6525 AJ Netherlands

Hippocampal reactivation of waking neuronal assemblies in sleep is a key initial step of systems consolidation. Nevertheless, it is unclear whether reactivated assemblies are static or whether they reorganize gradually over prolonged sleep. We tracked reactivated CA1 assembly patterns over ∼20 h of sleep/rest periods and related them to assemblies seen before or after in a spatial learning paradigm using rats. We found that reactivated assembly patterns were gradually transformed and started to resemble those seen in the subsequent recall session. Periods of rapid eye movement (REM) sleep and non-REM (NREM) had antagonistic roles: whereas NREM accelerated the assembly drift, REM countered it. Moreover, only a subset of rate-changing pyramidal cells contributed to the drift, whereas stable-firing-rate cells maintained unaltered reactivation patterns. Our data suggest that prolonged sleep promotes the spontaneous reorganization of spatial assemblies, which can contribute to daily cognitive map changes or encoding new learning situations. © 2025 The Authors

关键词： bayesian decoding hidden Markov model hippocampus learning place cells reactivations REM replay sharp-wave ripples sleep

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Iterative Tomographic Solution of Integer Least Squares Problems With Applications to MIMO Detection

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2011年第8期5卷 1486-1496页

作者： Goldberger, Jacob Leshem, Amir Bar Ilan Univ Sch Engn IL-52900 Ramat Gan Israel

Least squares (LS) fitting is one of the most fundamental techniques in science and engineering. It is used to estimate parameters from multiple noisy observations. In many problems the parameters are known a priori to be bounded integer valued, or they come from a finite set of values on an arbitrary finite lattice. Integer least squares is also an important problem in multi-channel communication systems and GPS applications. In this case finding the closest vector becomes NP-hard problem. In this paper, we propose two novel algorithms, the Tomographic Least Squares Decoder (TLSD), that not only solves the ILS problem, better than other sub-optimal techniques, but can also provide the a posteriori probability distribution for each element in the solution vector and a belief propagation version termed two-dimensional belief propagation (2DBP). Both algorithms are based on reconstruction of the vector from multiple two-dimensional projections. The projections are carefully chosen to provide low computational complexity. We show that the projections are equivalent to the two-dimensional marginals of the soft zero forcing linear decoder. We also provide simulated experiments comparing the algorithm to other sub-optimal algorithms. We end with a discussion of possible extensions to coded systems and combinations with sphere decoding.

关键词： Approximate maximum likelihood bayesian decoding integer least squares multi-input-multi-output (MIMO) communication systems MIMO-orthogonal frequency-division multiplexing (OFDM) systems sparse linear equations subset sum problems

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Multiclass LS-SVMs: Moderated outputs and coding-decoding schemes

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NEURAL PROCESSING LETTERS 2002年第1期15卷 35-58页

作者： van Gestel, T Suykens, JAK Lanckriet, G Lambrechts, A de Moor, B Vandewalle, J Katholieke Univ Leuven Dept Elect Engn ESAT SISTA B-3001 Louvain Belgium

A common way of solving the multiclass categorization problem is to reformulate the problem into a set of binary classification problems. Discriminative binary classifiers like, e.g., Support Vector Machines (SVMs), directly optimize the decision boundary with respect to a certain cost function. In a pragmatic and computationally simple approach, Least Squares SVMs (LS-SVMs) are inferred by minimizing a related regression least squares cost function. The moderated outputs of the binary classifiers are obtained in a second step within the evidence framework. In this paper, Bayes' rule is repeatedly applied to infer the posterior multiclass probabilities, using the moderated outputs of the binary plug-in classifiers and the prior multiclass probabilities. This bayesian decoding motivates the use of loss function based decoding instead of Hamming decoding. For SVMs and LS-SVMs with linear kernel, experimental evidence suggests the use of one-versus-one coding. With a Radial Basis Function kernel one-versus-one and error correcting output codes yield the best performances, but simpler codings may still yield satisfactory results.

关键词： bayesian decoding discriminant analysis evidence framework Hamming decoding multiclass classification regression Support Vector Machines

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MODELING OF GRID CELL ACTIVITY DEMONSTRATES IN VIVO ENTORHINAL 'LOOK-AHEAD' PROPERTIES

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NEUROSCIENCE 2013年 247卷 395-411页

作者： Gupta, K. Erdem, U. M. Hasselmo, M. E. Boston Univ Ctr Memory & Brain Boston MA 02215 USA

Recent in vivo data show ensemble activity in medial entorhinal neurons that demonstrates 'look-ahead' activity, decoding spatially to reward locations ahead of a rat deliberating at a choice point while performing a cued, appetitive T-Maze task. To model this experiment's look-ahead results, we adapted previous work that produced a model where scans along equally probable directions activated place cells, associated reward cells, grid cells, and persistent spiking cells along those trajectories. Such look-ahead activity may be a function of animals performing scans to reduce ambiguity while making decisions. In our updated model, look-ahead scans at the choice point can activate goal-associated reward and place cells, which indicate the direction the virtual rat should turn at the choice point. Hebbian associations between stimulus and reward cell layers are learned during training trials, and the reward and place layers are then used during testing to retrieve goal-associated cells based on cue presentation. This system creates representations of location and associated reward information based on only two inputs of heading and speed information which activate grid cell and place cell layers. We present spatial and temporal decoding of grid cell ensembles as rats are tested with perfect and imperfect stimuli. Here, the virtual rat reliably learns goal locations through training sessions and performs both biased and unbiased look-ahead scans at the choice point. Spatial and temporal decoding of simulated medial entorhinal activity indicates that ensembles are representing forward reward locations when the animal deliberates at the choice point, emulating in vivo results. (C) 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

关键词： entorhinal cortex grid cells look-ahead persistent spiking bayesian decoding place cells

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Multiscale relevance and informative encoding in neuronal spike trains

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JOURNAL OF COMPUTATIONAL NEUROSCIENCE 2020年第1期48卷 85-102页

作者： Cubero, Ryan John Marsili, Matteo Roudi, Yasser Norwegian Univ Sci Kavli Inst Syst Neuroscience Centre Neural ComputatTechnolNTNU Trondheim Norway Abdus Salam Int Ctr Theoret Phys Trieste Italy Scuola InterNazl Superiore Studi Avanzati Trieste Italy IST Austria Klosterneuburg Austria Ist Nazl Fis Nucl INFN Sezione Di Trieste Italy

Neuronal responses to complex stimuli and tasks can encompass a wide range of time scales. Understanding these responses requires measures that characterize how the information on these response patterns are represented across multiple temporal resolutions. In this paper we propose a metric - which we call multiscale relevance (MSR) - to capture the dynamical variability of the activity of single neurons across different time scales. The MSR is a non-parametric, fully featureless indicator in that it uses only the time stamps of the firing activity without resorting to any a priori covariate or invoking any specific structure in the tuning curve for neural activity. When applied to neural data from the mEC and from the ADn and PoS regions of freely-behaving rodents, we found that neurons having low MSR tend to have low mutual information and low firing sparsity across the correlates that are believed to be encoded by the region of the brain where the recordings were made. In addition, neurons with high MSR contain significant information on spatial navigation and allow to decode spatial position or head direction as efficiently as those neurons whose firing activity has high mutual information with the covariate to be decoded and significantly better than the set of neurons with high local variations in their interspike intervals. Given these results, we propose that the MSR can be used as a measure to rank and select neurons for their information content without the need to appeal to any a priori covariate.

关键词： Time series analysis Multiple time scale analysis Spike train data Information theory bayesian decoding

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MIMO decoding BASED ON STOCHASTIC RECONSTRUCTION FROM MULTIPLE PROJECTIONS

MIMO DECODING BASED ON STOCHASTIC RECONSTRUCTION FROM MULTIP...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Leshem, Amir Goldberger, Jacob Bar Ilan Univ Sch Engn IL-52100 Ramat Gan Israel

ISBN: (纸本)9781424423538

Least squares (LS) fitting is one of the most fundamental techniques in science and engineering. It is used to estimate parameters from multiple noisy observations. In many problems the parameters are known a-priori to be bounded integer valued, or they come from a finite set of values on an arbitrary finite lattice. In this case finding the closest vector becomes NP-Hard problem. In this paper we propose a novel algorithm, the Tomographic Least Squares Decoder (TLSD), that not only solves the ILS problem, better than other sub-optimal techniques, but also is capable of providing the a-posteriori probability distribution for each element in the solution vector. The algorithm is based on reconstruction of the vector from multiple two-dimensional projections. The projections art. carefully chosen to provide low computational complexity. Unlike other iterative techniques, such as the belief propagation, the proposed algorithm has ensured convergence. We also provide simulated experiments comparing the algorithm to other sub-optimal algorithms.

关键词： Integer Least Squares bayesian decoding sparse linear equations MIMO communication systems

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MIMO DETECTION BASED ON AVERAGING GAUSSIAN PROJECTIONS

MIMO DETECTION BASED ON AVERAGING GAUSSIAN PROJECTIONS

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Goldberger, Jacob Bar Ilan Univ Fac Engn IL-52100 Ramat Gan Israel

ISBN: (纸本)9781479928934

We propose a new detection algorithm for MIMO communication systems employing a two-dimensional marginal of the Gaussian approximation of the exact discrete distribution of the transmitted data given the received data. From the 2D distributions we derive one-dimensional marginals by averaging all the 2D joint distributions related to a single input symbol. We prove that this strategy to obtain a 1D distribution from a set of not necessarily consistent 2D distributions is optimal (for a specified criterion). The improved performance of the proposed algorithm is demonstrated on several instances of the problem of MIMO detection.

关键词： Integer Least Squares bayesian decoding MIMO communication systems

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Estimation bias and serial dependence in speed perception

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BMC PSYCHOLOGY 2024年第1期12卷 1-11页

作者： Wang, Si-Yu Zhang, Xiao-Yan Sun, Qi Zhejiang Normal Univ Sch Psychol Jinhua Peoples R China Intelligent Lab Zhejiang Prov Mental Hlth & Crisis Jinhua Peoples R China Zhejiang Normal Univ Key Lab Intelligent Educ Technol & Applicat Zhejia Jinhua Peoples R China

Studies have found that feature estimates are systematically compressed towards the distribution center, showing a central tendency. Additionally, the estimate of current features is affected by the previously seen feature, showing serial dependence or adaptation effect. However, these all remain unclear in the speed estimation. To address this question, we asked participants to estimate the speed of moving Gabor patches. In Experiment 1, speeds were selected from three uniform distributions with different lower and upper boundaries (i.e., slow, moderate, and fast ranges). In Experiment 2, speeds were arranged in an increasing, uniform, or decreasing distribution. The boundaries of three distributions were the same. The results found that speed estimates were systematically compressed towards the center of the uniform distribution center, showing a central tendency, and its size increased with the range boundaries. However, in the decreasing and increasing distributions, aside from central tendency, the speed estimates were also showed a bias away from the heavy tail of the distributions. Moreover, there was an attractive serial dependence that was not affected by the speed range. In summary, the current study, along with previous studies that reveal a slow-speed bias, comprehensively reveals various estimation biases in speed perception.

关键词： Speed perception Central tendency Serial dependence Slow-speed bias Efficient encoding bayesian decoding

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